English Abstract

In this research, an artificial intelligence (AI) model has been created to estimate the production rate of each layer in a multi-layered gas reservoir using static properties such as those obtained from well logging, in addition to dynamic properties such as pressure. This approach will be helpful in several reservoir engineering applications, such as understanding layers’ depletion, or targeting specific layers for workover. It could also be used for PLT analysis where the measured PLT values are compared to the expected values and a variance analysis could be performed.
Data were collected from more than 100 wells in a certain reservoir spanning over four fields. They were combined in related input variables and fed to the AI model for learning purposes. To compare different AI methods, the data were fed to 5 methods, namely ANFIS, MLP, RBF, SVM, and GRNN, and results were optimized for each method.
Between the tested AI methods, SVM and GRNN performed best as shown by a low mean absolute percentage error and a very high correlation coefficient. This research shows promising use for AI methods in estimating production rate from each layer in a multi-layered gas reservoir.